tf.contrib.layers.instance_norm获取均值和var？2.1885605296772486，我是否会错过与批处理维度有关的内容？代码：
import tensorflow as tf
import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
np.random.seed(2019)
def get_data_batch():
bs = 1
h = 3
w = 3
c = 4
x_np = np.random.rand(bs, h, w, c)
print('x_np.shape', x_np.shape)
return x_np
def my_instance_norm(x_np):
print('='*60)
with tf.Session() as sess:
print('np.sum(x_np)', np.sum(x_np))
x_tf = tf.convert_to_tensor(x_np)
x_m, x_v = tf.nn.moments(x_tf, [0, 1, 2])
x_std = tf.sqrt(x_v + 1e-8)
x_normalized = (x_tf - x_m) / x_std
x_normalized_np, x_m_np, x_v_np = sess.run(fetches=[x_normalized, x_m, x_v], feed_dict={x_tf: x_np})
print('x_m_np.shape', x_m_np.shape)
print('x_m_np:', x_m_np)
print('x_v_np.shape', x_v_np.shape)
print('x_v_np:', x_v_np)
print('x_normalized_np.shape:', x_normalized_np.shape)
print('x_normalized_np:', x_normalized_np)
return x_normalized_np
def tensorflow_instance_norm(x_np):
print('=' * 60)
# https://www.tensorflow.org/versions/r1.14/api_docs/python/tf/contrib/layers/instance_norm?hl=ca
with tf.Session() as sess:
print('np.sum(x_np)', np.sum(x_np))
x_tf = tf.convert_to_tensor(x_np)
z_tf = tf.contrib.layers.instance_norm(x_tf,
center=True,
scale=True,
epsilon=1e-06,
activation_fn=None,
param_initializers=None,
reuse=None,
variables_collections=None,
outputs_collections=None,
trainable=True,
data_format="NHWC",
scope="instance_norm_scope")
sess.run(tf.global_variables_initializer())
z_np = sess.run(fetches=[z_tf], feed_dict={x_tf: x_np})[0]
print('z_np.shape', z_np.shape)
print('z_np', z_np)
for item in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='instance_norm_scope'):
print('-'*60)
print(item.name)
print(type(item))
var = sess.run(item)
print(var.shape)
print(var)
return z_np
x_np = get_data_batch()
out_1 = my_instance_norm(x_np)
out_2 = tensorflow_instance_norm(x_np)
print('abs diff:', np.max(np.fabs(out_1-out_2)))
输出示例：
x_np.shape (1, 3, 3, 4)
============================================================
np.sum(x_np) 20.481414243908816
x_m_np.shape (4,)
x_m_np: [0.56814751 0.50049309 0.50423491 0.70283719]
x_v_np.shape (4,)
x_v_np: [0.08732506 0.05503668 0.07861654 0.09638579]
x_normalized_np.shape: (1, 3, 3, 4)
x_normalized_np: [[[[ 1.13477312 -0.45785613 0.42703584 -0.20923679]
[ 1.05699812 -0.85814979 0.70603761 0.64539253]
[ 1.05998571 -0.4038521 -0.18470326 -1.40361391]]
[[-1.37147699 1.65696195 -1.26881563 0.90796148]
[-1.81310153 0.06333216 -1.08102901 0.59000616]
[-0.18452143 0.33166593 -0.73096354 0.43277698]]
[[-0.14042819 -1.68649001 -0.80640672 -2.11376961]
[-0.19989755 -0.11963737 1.42750286 0.77515475]
[ 0.45766874 1.47402535 1.51134183 0.37532841]]]]
============================================================
np.sum(x_np) 20.481414243908816
z_np.shape (1, 3, 3, 4)
z_np [[[[ 1.13476668 -0.45785201 0.42703315 -0.20923572]
[ 1.05699213 -0.85814207 0.70603317 0.64538921]
[ 1.0599797 -0.40384847 -0.18470209 -1.40360671]]
[[-1.37146922 1.65694705 -1.26880764 0.90795682]
[-1.81309125 0.06333159 -1.0810222 0.59000313]
[-0.18452038 0.33166295 -0.73095893 0.43277476]]
[[-0.14042739 -1.68647484 -0.80640164 -2.11375875]
[-0.19989642 -0.11963629 1.42749387 0.77515077]
[ 0.45766614 1.47401209 1.51133232 0.37532648]]]]
------------------------------------------------------------
instance_norm_scope/beta:0
<class 'tensorflow.python.ops.variables.RefVariable'>
(4,)
[0. 0. 0. 0.]
------------------------------------------------------------
instance_norm_scope/gamma:0
<class 'tensorflow.python.ops.variables.RefVariable'>
(4,)
[1. 1. 1. 1.]
abs diff: 1.5168087757144733e-05
答案 0 :(得分：1)
固定版本：max abs diff: 4.440892098500626e-16
import tensorflow as tf
import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
np.random.seed(2019)
EPS = 1e-3
print('tf.__version__', tf.__version__)
def get_data_batch():
bs = 2
h = 3
w = 3
c = 4
x_np = np.random.rand(bs, h, w, c)
print('x_np.shape', x_np.shape)
return x_np
def my_instance_norm(x_np):
print('='*60)
with tf.Session() as sess:
print('np.sum(x_np)', np.sum(x_np))
x_tf = tf.convert_to_tensor(x_np)
x_m, x_v = tf.nn.moments(x_tf, [1, 2], keep_dims=True)
x_std = tf.sqrt(x_v + EPS)
x_normalized = (x_tf - x_m) / x_std
x_normalized_np, x_m_np, x_v_np = sess.run(fetches=[x_normalized, x_m, x_v], feed_dict={x_tf: x_np})
print('x_m_np.shape', x_m_np.shape)
print('x_m_np:', x_m_np)
print('x_v_np.shape', x_v_np.shape)
print('x_v_np:', x_v_np)
print('x_normalized_np.shape:', x_normalized_np.shape)
print('x_normalized_np:', x_normalized_np)
return x_normalized_np
def tensorflow_instance_norm(x_np):
print('=' * 60)
# https://www.tensorflow.org/versions/r1.14/api_docs/python/tf/contrib/layers/instance_norm?hl=ca
with tf.Session() as sess:
print('np.sum(x_np)', np.sum(x_np))
x_tf = tf.convert_to_tensor(x_np)
z_tf = tf.contrib.layers.instance_norm(x_tf,
center=True,
scale=True,
epsilon=EPS,
activation_fn=None,
param_initializers=None,
reuse=None,
variables_collections=None,
outputs_collections=None,
trainable=True,
data_format="NHWC",
scope="instance_norm_scope")
sess.run(tf.global_variables_initializer())
z_np = sess.run(fetches=[z_tf], feed_dict={x_tf: x_np})[0]
print('z_np.shape', z_np.shape)
print('z_np', z_np)
for item in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='instance_norm_scope'):
print('-'*60)
print(item.name)
print(type(item))
var = sess.run(item)
print(var.shape)
print(var)
return z_np
def run_test():
x_np = get_data_batch()
out_1 = my_instance_norm(x_np)
out_2 = tensorflow_instance_norm(x_np)
print('max abs diff:', np.max(np.fabs(out_1-out_2)))
run_test()